CAJ | 학술논문

故障电弧是引发电气火灾事故的主要因为之一.本文将自组织映射神经网络引入故障电弧研究领域,进行不同负荷情况下的故障电弧聚类分析.首先参照美国UL1699标准进行实验采集电流数据,然后利用自组织映射神经网络实现可视化聚类,并结合k均值法确定聚类结果.根据聚类结果分析故障电弧,对比故障与正常时的差异所在,提取故障电弧的典型特征.最后总结出故障电弧电流通常具有电流短时为零、正负半周差异大、幅值变化大等特征,为故障电弧保护技术提供参考.
고장전호시인발전기화재사고적주요인위지일.본문장자조직영사신경망락인입고장전호연구영역,진행불동부하정황하적고장전호취류분석.수선삼조미국UL1699표준진행실험채집전류수거,연후이용자조직영사신경망락실현가시화취류,병결합k균치법학정취류결과.근거취류결과분석고장전호,대비고장여정상시적차이소재,제취고장전호적전형특정.최후총결출고장전호전류통상구유전류단시위령、정부반주차이대、폭치변화대등특정,위고장전호보호기술제공삼고.
Arc fault is one of the prime reasons causing electrical fire accidents.This paper carries out cluster analysis of arc faults under different loads through using self-organizing map neural network.Firstly,data are collected in the experiments based on UL1699.1hen clustering is visualized using SOM neural network and K-means cluster.According to the clustering results,some typical characters of arc fault are found,such as short-time zero current,asymmetrical waveform and amplitude variation,which can provide a reference for arc fault protection.